Tag Archive | Customer Experience Strategy

Business Case and Implications for Consistency – Part 7 – Disparate Treatment of Protected Classes

Previously we explored the business case for consistency both within individual channels and across multiple channels.  In this post, we will explore consistency of treatment in a demographic context.

Inconsistent treatment based on certain demographic characteristics is illegal.  The Civil Rights Act of 1964 prohibits discrimination in almost all privately owned service industries based on race, color, religion, gender, or national origin.  Other industries, such as retail banking, have additional regulatory requirements.

Beyond this legal risk, managers must be aware of the significant risk to the reputation of the brand posed by discriminatory practices.

Managers may seek comfort in the knowledge that their company’s policies and procedures are not to refuse service to anyone.  However, this overt discrimination is just a small part of the risk associated with discrimination.  Beyond overt discrimination, which is extremely rare, there are two other categories of discriminatory practices: disparate impact and disparate treatment.

Disparate impact is the result of policies or business practices which have an unequal impact.  A restaurant with a policy to require prepayment for meals from one demographic group and not another is an example of disparate impact.

Disparate treatment is differences in treatment that originate at the customer-employee interface.  Disparate treatment does not necessarily need to be a conscious act.  It can be an unconscious pattern or practice of different treatment that the employee is not even aware of.  The use of name, offering promotional material to a customer of one group as opposed to a customer on another group are all examples of disparate treatment.

Now, observing differences is treatment is not necessarily proof of discrimination.  Human behavior, after all, is variable.  There is a certain amount of normal variation in all service encounters.  The trick is to determine if disparate treatment observed represents a pattern or practice of discrimination.  Fortunately statistics has the answer, we use statistical tests of significance to determine both if observed differences in treatment are the result of actual discriminatory practices and the likelihood that any one member of a protected class will be treated differently than a member of another protected class.  It should be noted, however, that regulatory agencies set the bar much higher.  Many do not necessarily rely on statistical testing.  In their view, any single case of disparate treatment is evidence of discrimination.

In a future post we will discuss the implications for customer experience researchers in testing for disparate treatment.

Business Case and Implications for Consistency – Part 6 – Intra-Channel Consistency

Previously we explored inter-channel consistency and its implication for customer experience managers.

Inconsistent customer experiences are a significant threat to customer loyalty.  In a previous post, we observed the casual relationship between consistency in the customer experience and feelings of trust and loyalty.

Consistency drives satisfaction.  It is extremely common to see a correlation between intra-channel consistency and performance.  Consider the following scatter plot from Kinesis’ research, which plots bank branch customer satisfaction by the variation in branch customer satisfaction:

Branch Satisfaction by VariationAs this plot demonstrates, consistency correlates with quality.  Branches with higher customer satisfaction ratings are also the most consistent.  In our customer experience research proactive we see this time and time again.

Additionally, this plot also demonstrates that top-line averages of customer satisfaction can be misleading.  The bank in this plot had an average customer satisfaction rating of 93%.  However, many branches fall well below this top-line average, resulting in an incomplete picture of the customer experience.  Customers do not experience top-line averages; they experience the customer experience one interaction at a time at the local business unit level.

What are the implications for managers of the customer experience?

The first implication for managers is the above observation that top-line averages can mislead.  Top-line averages hide individual business units with both low and inconsistent customer satisfaction.  Top-line averages come between management and customers, distancing managers from how customers actually experience the brand.

Secondly, variation must be managed at the cause.  Intra-channel variation is almost always at the local business unit level.  For example, a store with a high degree of variation in customer traffic will experience a high degree of variation in the customer experience if management does not mitigate the effects of the variation in traffic.

How to manage for consistency:

  1. Manage inconsistency at the cause
  2. Write a clear mission statement
  3. Use appropriate analytics
  4. Don’t silo analytics by channel
  5. Meet regularly with employees to share problems and potential solutions
  6. Focus on customer journey

Intra-channel consistency needs to be managed at the local level – individual stores and agents.  Tools need to be available deep into the organization to allow managers at the lowest level of each channel to deliver a consistent experience.

In the next post we will explore demographic consistency, treating all customers the same regardless of their demographic profile.

Business Case and Implications for Consistency – Part 5 – Inter-Channel Consistency

Previously we explored the business case for consistency by considering the influence of poor experiences.

The modern customer experience environment is constituted of an ever expanding variety of delivery channels, with no evidence of the slowing of the pace of channel expansion.  As channel expansion continues, customer empowerment is increasing with customer choice.  Customer relationships with brands are not derived from individuals’ discrete interactions.  Rather, customer relationships are defined by clusters of interactions, clusters of interactions across the entire life cycle of the relationships, and across all channels.  Inter-channel consistency defines the customer relationship.

McKinsey and Company concluded in their 2014 report, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, demonstrated, in a retail banking context, a link between cross-channel consistency and bank performance.

In customers’ minds, all channels belong to the same brand.  Customers do not consider management silos or organizational charts – to them all channels are the same.  Customers expect consistent experiences regardless of channel.  In their minds, an agent at a call center should have the same information and training as in-person agents.

What are the implications for managers of the customer experience?

The primary management issue in aligning disparate channels is to manage inconsistency at its cause.  The most common cause of inconsistencies across channels is the result of siloed management, where managers’ jurisdiction is limited to their channel. Inter-channel consistency is increasingly important as advances in technology expand customer choice.  Brands need to serve customers in the channel of their choice.   Therefore, the cause of inter-channel inconsistency must be managed higher up in the organization at the lowest level where lines of authority across channels converge, or through some kind of cross-functional authority.

The implications for management are not limited to senior management and cross-functional teams. Customer experience managers should be aware that top-line averages can mislead.  Improvement opportunities are rarely found in top-line averages, but at the local level.  Again, the key is to manage inconsistency at the cause.  Inconsistency at the local level almost always has a local cause; as a result, variability in performance must be managed at the local level as well.

In a previous post from 2014, we discussed aligning cross channel service behaviors and attributes.

In the next blog post in this series, we will explore intra-channel consistency.

Business Case and Implications for Consistency – Part 4 – Consistency and the Outsized Influence of Poor Experiences

In earlier posts we discussed the business case for consistency, primarily because consistency drives customer loyalty and the causal chain from consistency to customer loyalty.

This post continues to explore the business case for consistency by considering the influence of poor experiences.

To start, let’s consider the following case study:

Assume a brand’s typical customer has 5 service interactions per year.  Also assume, the brand has a relatively strong 95% satisfaction rate.  Given these assumptions, the typical customer has a 25% probability each year of having a negative experience, and in four years, in theory, every customer will have a negative experience.

In 4 Years: Every Customer Will Have a Negative Experience

As this case study illustrates, customer relationships with brands are not defined by individual, discrete customer experiences but by clusters of interactions across the lifecycle of the customer relationship.  The influence of individual experiences is far less important than the cumulative effect of these clusters of customer experiences.

Consistency reduces the likelihood of negative experiences contaminating the clusters of experiences which make up the whole of the customer relationship.  Negative experiences, regardless of how infrequent, have a particularly caustic effect on the customer relationship.   A variety of research, including McKiney’s The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, has concluded that negative experiences have three to four times the influence on the customer as positive experiences – three to four times the influence on the customer’s emotional reaction to the brand – three to four times the influence on loyalty, purchase intent and social sharing within their network.

Negative Experiences Outweigh Positive Experiences

 

In our next post we will discuss inter-channel consistency.

Business Case and Implications for Consistency – Part 3: The Causal Chain from Consistency to Customer Loyalty

In an earlier post we discussed the business case for consistency, primarily because consistency drives customer loyalty.  This post describes the causal chain from consistency to customer loyalty.

Brands are defined by how customers experience them, and they will have both an emotional and behavioral reaction to what they experience.  It is these reactions to the customer experience which drive satisfaction, loyalty and profitability.

There is a causal chain from consistency to customer loyalty.  McKinsey and Company concluded in their 2014 report, The Three Cs of Customer Satisfaction: Consistency, Consistency, Consistency, that feelings of trust are the strongest drivers of customer satisfaction and loyalty, and consistency is central to building customer trust.

For example, in our experience in the banking industry, institutions in the top quartile of consistent delivery are 30% more likely to be trusted by their customers compared to the bottom quartile.  Furthermore, agreement with the statements: my bank is “a brand I feel close to” and “a brand that I can trust” are significant drivers of brand differentiation as a result of the customer experience.  Again, brands are defined by how customers experience them.  In today’s environment where consumer trust in financial institutions is extremely low, fostering trust is critical for driving customer loyalty.  Consistency fosters trust.  Trust drives loyalty.

In our next post we will continue to explore the business case for consistency by considering the influence of poor experiences.

 

Business Case and Implications for Consistency – Part 1: Why We Value Consistency

Humans value consistency – we are hard wired to do so – it’s in our DNA.

It is generally believed that modern humans originated on the Savanna Plain. Life was difficult for our distant forefathers. Sources of water, food, shelter were unreliable. Dangers existed at every turn. Evolving in this unreliable and hostile environment, evolutionary forces selected in modern humans a value for consistency – in effect hard wiring us to value consistency. We seek security in an insecure world.

In this context, it is not surprising we evolved to value consistency. While our modern world is a far more reliable environment, our brains are still hard wired to value consistency.

The implication for managers of the customer experience is obvious – customers want and value consistency in the customer experience. We’ve all felt it. When a car fails to start, when the power goes out, when software crashes we all feel uncomfortable. A lack of reliability and consistency creates confusion and frustration. We want to have confidence that reliable events like starting the car, turning on the lights or using software will work consistently. In the customer experience realm, we want to have confidence that the brands we have relationships with will deliver consistently on their brand promise each time without variation in quality.

Customers expect consistent delivery on the brand promise. They base their expectations on prior experience. Thus customers are in a self-reinforcing cycle where expectations are set based on prior experiences continually reinforcing the importance of consistency. This is the foundation of customer loyalty. We are creates of habit. The foundation of customer loyalty is built on the foundation of dependable, consistent, quality service delivery.

While we evolved in a difficult and unreliable environment, our modern society is much more reliable. Our modern society offers a much more consistent existent. Again, it’s a self-reinforcing cycle. Product quality and consistency of our mass production economy has reinforced our expectations of consistency.

Today’s information technology continues to reinforce our desire for consistency. However, it adds an additional element of customization. Henry Ford, the father of mass production, famously said of the Model-T, “You can have any color you want as long as it’s black.” Those days are gone. Today, we expect both consistency and customization.

In the next post, we will explore the business case for consistency.

Mystery Shopping Gap Analysis: Identify Service Attributes with Highest Potential for ROI

Research without call to action may be interesting, but in the end, not very useful.

This is particularly true with customer experience research.  It is incumbent on customer experience researchers to give management research tools which will identify clear call to action items –items in which investments will yield the highest return on investment (ROI) in terms of meeting management’s customer experience objectives.   This post introduces a simple intuitive mystery shopping analysis technique that identifies the service behaviors with the highest potential for ROI in terms of achieving these objectives.

Mystery shopping gap analysis is a simple three-step analytical technique.

Step 1: Identify the Key Objective of the Customer Experience

The first step is to identify the key objective of the customer experience.  Ask yourself, “How do we want the customer to think, feel or act as a result of the customer experience?”

For example:

  • Do you want the customer to have increased purchase intent?
  • Do you want the customer to have increased return intent?
  • Do you want the customer to have increased loyalty?

Let’s assume the key objective is increased purchase intent.  At the conclusion of the customer experience you want the customer to have increased purchase intent.

Next draft a research question to serve as a dependent variable measuring the customer’s purchase intent.  Dependent variables are those which are influenced or dependent on the behaviors measured in the mystery shop.

Step 2: Determine Strength of the Relationship of this Key Customer Experience Objective

After fielding the mystery shop study, and collecting a statistically significant number of shops, the next step is to determine the strength of the relationship between this key customer experience measure (the dependent variable) and each behavior or service attribute measured (independent variable).  There are a number of ways to determine the strength of the relationship, perhaps the easiest is a simple cross-tabulation of the results.  Cross tabulation groups all the shops with positive purchase intent and all the shops with negative purchase intent together and makes comparisons between the two groups.  The greater the difference in the frequency of a given behavior or service attribute between shops with positive purchase intent compared to negative, the stronger the relationship to purchase intent.

The result of this cross-tabulation yields a measure of the importance of each behavior or service attribute.  Those with stronger relationships to purchase intent are deemed more important than those with weaker relationships to purchase intent.

Step 3: Plot the Performance of Each Behavior Relative to Its Relationship to the Key Customer Experience Objective

The third and final step in this analysis to plot the importance of each behavior relative to the performance of each behavior together on a 2-dimensional quadrant chart, where one axis is the importance of the behavior and the other is its performance or the frequency with which it is observed.

Interpretation

Interpreting the results of this quadrant analysis is fairly simple.    Behaviors with above average importance and below average performance are the “high potential” behaviors.  These are the behaviors with the highest potential for return on investment (ROI) in terms of driving purchase intent.  These are the behaviors to prioritize investments in training, incentives and rewards.  These are the behaviors which will yield the highest ROI.

The rest of the behaviors are prioritized as follows:

Those with the high importance and high performance are the next priority.  They are the behaviors to maintain.  They are important and employees perform them frequently, so invest to maintain their performance.

Those with low importance are low performance are areas to address if resources are available.

Finally, behaviors or service attributes with low importance yet high performance are in no need of investment.  They are performed with a high degree of frequency, but not very important, and will not yield an ROI in terms of driving purchase intent.

Research without call to action may be interesting, but in the end, not very useful.

This simple, intuitive gap analysis technique will provide a clear call to action in terms of identifying service behaviors and attributes which will yield the most ROI in terms of achieving your key objective of the customer experience.

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